Relation Between Inferential Statistics and machine Learning

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Relation Between Inferential Statistics and machine Learning

Data preprocessing

outlier detection methods can help identify and handle anomalous data points, imputation techniques can fill in missing values, and data transformation methods can normalize variables or address skewness. These preprocessing steps improve the quality and reliability of the data, leading to better model performance.

Feature selection

Techniques like correlation analysis, chi-square tests, and analysis of variance (ANOVA) can be used to determine the statistical relationships between variables and their relevance for prediction. Feature selection based on inferential statistics helps in reducing dimensionality, improving model interpretability, and potentially enhancing model efficiency.

Model assessment and validation

Cross-validation methods, hypothesis testing, and statistical significance testing can be applied to assess model accuracy, determine if the model’s performance is better than random chance, and compare different models. These statistical assessments help validate the models, quantify uncertainty, and assess their generalizability to unseen data.

Interpretability and explanation

Statistical techniques can help identify influential variables, assess the strength and significance of relationships, and quantify the effects of predictors on the target variable. This interpretability provides insights into the model’s behavior and helps build trust and understanding among users and stakeholders.

Causal inference

Inferential statistics play a crucial role in establishing causal relationships between variables. While machine learning and deep learning models are primarily focused on prediction, inferential statistics allow for causal inference through experimental design, observational studies, and
other statistical techniques.

Robustness and generalization

Techniques such as confidence intervals and hypothesis testing allow for quantifying uncertainty and assessing the reliability of predictions, making the models more robust and trustworthy.

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